Evidence map›Paper›PMID 30940752›Full record

ArticleBMJ open2019

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

Beata Fonferko-Shadrach, Arron S Lacey, Angus Roberts, Ashley Akbari, Simon Thompson, David V Ford, Ronan A Lyons, Mark I Rees, William Owen Pickrell

Abstract read
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Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  13. Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.Genetics in medicine : official journal of the American College of Medical Genetics · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Beata Fonferko-Shadrach *Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.
Arron S Lacey *Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.
Angus RobertsInstitute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Ashley AkbariHealth Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK.
Simon ThompsonHealth Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK.
David V FordHealth Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK.
Ronan A LyonsHealth Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK.
Mark I ReesNeurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.
William Owen PickrellNeurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.

Funding

Arthritis Research UKBritish Heart FoundationCancer Research UKMedical Research Council MC_PC_17214Medical Research Council MR/K006525/1
6 · The paper itself

Abstract

objectiveRoutinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techniques to extract detailed clinical information from epilepsy clinic letters to enrich routinely collected data.

designWe used the general architecture for text engineering (GATE) framework to build an information extraction system, ExECT (extraction of epilepsy clinical text), combining rule-based and statistical techniques. We extracted nine categories of epilepsy information in addition to clinic date and date of birth across 200 clinic letters. We compared the results of our algorithm with a manual review of the letters by an epilepsy clinician.

settingDe-identified and pseudonymised epilepsy clinic letters from a Health Board serving half a million residents in Wales, UK.

resultsWe identified 1925 items of information with overall precision, recall and F1 score of 91.4%, 81.4% and 86.1%, respectively. Precision and recall for epilepsy-specific categories were: epilepsy diagnosis (88.1%, 89.0%), epilepsy type (89.8%, 79.8%), focal seizures (96.2%, 69.7%), generalised seizures (88.8%, 52.3%), seizure frequency (86.3%-53.6%), medication (96.1%, 94.0%), CT (55.6%, 58.8%), MRI (82.4%, 68.8%) and electroencephalogram (81.5%, 75.3%).

conclusionsWe have built an automated clinical text extraction system that can accurately extract epilepsy information from free text in clinic letters. This can enhance routinely collected data for research in the UK. The information extracted with ExECT such as epilepsy type, seizure frequency and neurological investigations are often missing from routinely collected data. We propose that our algorithm can bridge this data gap enabling further epilepsy research opportunities. While many of the rules in our pipeline were tailored to extract epilepsy specific information, our methods can be applied to other diseases and also can be used in clinical practice to record patient information in a structured manner.

Indexed as

Information Storage and RetrievalMedical RecordsNatural Language ProcessingAlgorithmsElectroencephalographyElectronic Health RecordsEpilepsyHumansMagnetic Resonance ImagingSeizuresWalesepilepsyinformation extractionnatural language processingvalidation

Identifiers

PMID30940752
PMCPMC6500195

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.